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PREDICTING ABNORMAL STOCK RETURN VOLATILITY USING TEXTUAL ANALYSIS OF NEWS - A META-LEARNING APPROACH

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F18%3A39913349" target="_blank" >RIV/00216275:25410/18:39913349 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.amfiteatrueconomic.ro/ArticolEN.aspx?CodArticol=2703" target="_blank" >http://www.amfiteatrueconomic.ro/ArticolEN.aspx?CodArticol=2703</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.24818/EA/2018/47/185" target="_blank" >10.24818/EA/2018/47/185</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    PREDICTING ABNORMAL STOCK RETURN VOLATILITY USING TEXTUAL ANALYSIS OF NEWS - A META-LEARNING APPROACH

  • Original language description

    Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal stock price behavior subsequent to their dissemination. However, previous studies to date have tended to focus on linear regression methods in predicting volatility. Here, we show that non-linear models can be effectively employed to explain the residual variance of the stock price. Moreover, we use meta-learning approach to simulate the decision-making process of various investors. The results suggest that this approach significantly improves the prediction accuracy of abnormal stock return volatility. The fact that the length of news articles is more important than news sentiment in predicting stock return volatility is another important finding. Notably, we show that Rotation forest performs particularly well in terms of both the accuracy of abnormal stock return volatility and the performance on imbalanced volatility data.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50204 - Business and management

Result continuities

  • Project

    <a href="/en/project/GA16-19590S" target="_blank" >GA16-19590S: Topic and sentiment analysis of multiple textual sources for corporate financial decision-making</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Amfiteatru Economic

  • ISSN

    1582-9146

  • e-ISSN

  • Volume of the periodical

    20

  • Issue of the periodical within the volume

    47

  • Country of publishing house

    RO - ROMANIA

  • Number of pages

    17

  • Pages from-to

    185-201

  • UT code for WoS article

    000427829800012

  • EID of the result in the Scopus database

    2-s2.0-85041616998